In this notebook we conduct exploratory factor analyses (EFAs) on the datasets for our studies of concepts of mental life, in which each participants judged the various mental capacities of a particular target entity. We analyze datasets for adults and children from each of our five field sites: the US, Ghana, Thailand, China, and Vanuatu.

This notebook contains secondary analyses, parallel to the results presented in the main text, in which we dropped participants who gave the same answer on every trial.

Adults

Samples

  country   n
       US 106
    Ghana  88
 Thailand 144
    China 120
  Vanuatu 120
    Total 578

Scale use

Factor retention: parallel analysis

Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  3 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 

Exploratory factor analysis

Factor loadings

Congruence

Bootstrapped congruence

Children

Samples

  country   n
       US 115
    Ghana 129
 Thailand 151
    China 120
  Vanuatu 128
    Total 643

Scale use

Factor retention: parallel analysis

Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  4  and the number of components =  2 
Parallel analysis suggests that the number of factors =  3  and the number of components =  2 

Exploratory factor analysis

Factor loadings

Congruence

See All samples, below.

Bootstrapped congruence

All samples

Congruence

Jaccard Similarity

Developmental comparisons

Error: width must be of length 1 or ncol - 1

Variance accounted for

Interfactor correlations

---
title: "Concepts of mental life across cultures: Secondary analysis"
subtitle: "Exploratory factor analysis using full datasets, tetrachoric correlations, and oblique transformations"
authors: "**Kara Weisman**, Cristine H. Legare, Rachel E. Smith, Vivian A. Dzokoto, Felicity Aulino, Emily Ng, John C. Dulin, Nicole Ross-Zehnder, Joshua D. Brahinsky, & Tanya M. Luhrmann"
output: 
  html_notebook:
    toc: true
    toc_float: true
---

```{r setup}
knitr::opts_chunk$set(message = F, warning = FALSE)
```

In this notebook we conduct exploratory factor analyses (EFAs) on the datasets for our studies of concepts of mental life, in which each participants judged the various mental capacities of a particular target entity. We analyze datasets for adults and children from each of our five field sites: the US, Ghana, Thailand, China, and Vanuatu. 

This notebook contains secondary analyses, parallel to the results presented in the main text, in which we dropped participants who gave the same answer on every trial. 

```{r, echo = F, message = F}
source("./scripts/dependencies.R")
source("./scripts/custom_funs.R")
source("./scripts/var_recode_contrast.R")
```

```{r}
# function for eliminating participants who gave the same answer on every trial
detect_no_var_fun <- function(df, remove_na = T) {
  
  d_id <- df 
  
  if (remove_na) { d_id <- d_id %>% filter(!is.na(response)) }
  
  d_id <- d_id %>%
    distinct(subj_id, response) %>%
    count(subj_id) %>%
    filter(n == 1)
  
  d_res <- df %>%
    filter(!subj_id %in% d_id$subj_id)
  
  return(d_res)
}
```

```{r data}
# read in data, shorten "feel sick," and limit to universal targets and questions: adults
d_us_adults <- read_csv("../data/d_us_adults.csv") %>%
  filter(target %in% levels_target_univ, question_cat == "universal") %>%
  mutate(question = gsub("\\, .*$", " \\[...\\]", question)) %>%
  detect_no_var_fun()
d_gh_adults <- read_csv("../data/d_gh_adults.csv") %>%
  filter(target %in% levels_target_univ, question_cat == "universal") %>%
  mutate(question = gsub("\\, .*$", " \\[...\\]", question)) %>%
  detect_no_var_fun()
d_th_adults <- read_csv("../data/d_th_adults.csv") %>%
  filter(target %in% levels_target_univ, question_cat == "universal") %>%
  mutate(question = gsub("\\, .*$", " \\[...\\]", question)) %>%
  detect_no_var_fun()
d_ch_adults <- read_csv("../data/d_ch_adults.csv") %>%
  filter(target %in% levels_target_univ, question_cat == "universal") %>%
  mutate(question = gsub("\\, .*$", " \\[...\\]", question)) %>%
  detect_no_var_fun()
d_vt_adults <- read_csv("../data/d_vt_adults.csv") %>%
  filter(target %in% levels_target_univ, question_cat == "universal") %>%
  mutate(question = gsub("\\, .*$", " \\[...\\]", question)) %>%
  detect_no_var_fun()

# read in data, shorten "feel sick," and limit to universal targets and questions: children
d_us_children <- read_csv("../data/d_us_children.csv") %>%
  filter(target %in% levels_target_univ, question_cat == "universal") %>%
  mutate(question = gsub("\\, .*$", " \\[...\\]", question)) %>%
  detect_no_var_fun()
d_gh_children <- read_csv("../data/d_gh_children.csv") %>%
  filter(target %in% levels_target_univ, question_cat == "universal") %>%
  mutate(question = gsub("\\, .*$", " \\[...\\]", question)) %>%
  detect_no_var_fun()
d_th_children <- read_csv("../data/d_th_children.csv") %>%
  filter(target %in% levels_target_univ, question_cat == "universal") %>%
  mutate(question = gsub("\\, .*$", " \\[...\\]", question)) %>%
  detect_no_var_fun()
d_ch_children <- read_csv("../data/d_ch_children.csv") %>%
  filter(target %in% levels_target_univ, question_cat == "universal") %>%
  mutate(question = gsub("\\, .*$", " \\[...\\]", question)) %>%
  detect_no_var_fun()
d_vt_children <- read_csv("../data/d_vt_children.csv") %>%
  filter(target %in% levels_target_univ, question_cat == "universal") %>%
  mutate(question = gsub("\\, .*$", " \\[...\\]", question)) %>%
  # filter out participants outside of the age range
  filter((age >= 6 & age <= 12) | is.na(age)) %>%
  detect_no_var_fun()
```

```{r wide}
# make wide-form datasets for EFA: adults
d_us_adults_w <- wide_df_fun(d_us_adults)
d_gh_adults_w <- wide_df_fun(d_gh_adults)
d_th_adults_w <- wide_df_fun(d_th_adults)
d_ch_adults_w <- wide_df_fun(d_ch_adults)
d_vt_adults_w <- wide_df_fun(d_vt_adults)

# make wide-form datasets for EFA: children
d_us_children_w <- wide_df_fun(d_us_children)
d_gh_children_w <- wide_df_fun(d_gh_children)
# d_gh_eng_children_w <- wide_df_fun(d_gh_eng_children)
d_th_children_w <- wide_df_fun(d_th_children)
d_ch_children_w <- wide_df_fun(d_ch_children)
d_vt_children_w <- wide_df_fun(d_vt_children)
```


# Adults

## Samples

```{r samples adults}
bind_rows(d_us_adults, d_gh_adults, d_th_adults, d_ch_adults, d_vt_adults) %>%
  mutate(country = factor(country, levels = levels_country)) %>%
  distinct(country, subj_id) %>%
  count(country) %>%
  janitor::adorn_totals()
```

## Scale use

```{r scale use mean overall adults}
bind_rows(d_us_adults, d_gh_adults, d_th_adults, d_ch_adults, d_vt_adults) %>%
  mutate(country = factor(country, levels = levels_country),
         response_cat = recode_factor(response_cat,
                                      "no" = "no",
                                      "kind of" = "kind of",
                                      "yes" = "yes", 
                                      .missing = "missing data")) %>%
  count(country, response_cat) %>%
  complete(response_cat, nesting(country), fill = list(n = 0)) %>%
  group_by(country) %>%
  mutate(prop = n/sum(n, na.rm = T)) %>%
  ungroup() %>%
  select(-n) %>%
  spread(response_cat, prop) %>%
  janitor::adorn_pct_formatting(digits = 2)
```

## Factor retention: parallel analysis

```{r parallel dist adults, fig.width = 3, fig.asp = 0.5}
# NOTE: Here is distribution over outcomes of parallel analysis with 100 iterations. We'll choose the median number of factors.

if (file.exists("../results/pa_outcomes_dist_adults_drop.RDS")) {
  
  pa_outcomes_dist_adults <- readRDS("../results/pa_outcomes_dist_adults_drop.RDS")
  
} else {
  
  pa_outcomes_dist_adults <- data.frame(us = NULL, gh = NULL, th = NULL,
                                        ch = NULL, vt = NULL)
  
  set.seed(54321)
  n_cores <- parallel::detectCores()
  options(mc.cores = n_cores)
  
  for (i in 1:100) {
    pa_outcomes_dist_adults[i, "us"] <- fa.parallel(d_us_adults_w, plot = F)$nfact
    pa_outcomes_dist_adults[i, "gh"] <- fa.parallel(d_gh_adults_w, plot = F)$nfact     
    pa_outcomes_dist_adults[i, "th"] <- fa.parallel(d_th_adults_w, plot = F)$nfact
    pa_outcomes_dist_adults[i, "ch"] <- fa.parallel(d_ch_adults_w, plot = F)$nfact
    pa_outcomes_dist_adults[i, "vt"] <- fa.parallel(d_vt_adults_w, plot = F)$nfact
  }
  
  saveRDS(pa_outcomes_dist_adults, file = "../results/pa_outcomes_dist_adults_drop.RDS")
}

# plot
pa_outcomes_dist_adults %>%
  rownames_to_column("iter") %>%
  gather(country, nfact, -iter) %>%
  mutate(country = factor(country,
                          levels = c("us", "gh", "th", "ch", "vt"),
                          labels = levels_country)) %>%
  ggplot(aes(x = nfact)) +
  facet_grid(~ country) +
  geom_bar(stat = "count") +
  scale_x_continuous(limits = c(1, max(pa_outcomes_dist_adults) + 1),
                     breaks = seq(0, 100, 1)) +
  labs(x = "Number of factors suggested by fa.parallel()")
```

## Exploratory factor analysis

```{r efa adults}
set.seed(54321)

# do exploratory factor analysis: adults
efa_us_adults <- fa_fun(d_us_adults_w,
                        n = median(pa_outcomes_dist_adults$us),
                        chosen_n.iter = 1000,
                        chosen_rot = "oblimin")
colnames(efa_us_adults$loadings) <- paste0("usADULTS_", 
                                           colnames(efa_us_adults$loadings))

efa_gh_adults <- fa_fun(d_gh_adults_w, 
                        n = median(pa_outcomes_dist_adults$gh),
                        chosen_n.iter = 1000,
                        chosen_rot = "oblimin")
colnames(efa_gh_adults$loadings) <- paste0("ghADULTS_", 
                                           colnames(efa_gh_adults$loadings))

efa_th_adults <- fa_fun(d_th_adults_w, 
                        n = median(pa_outcomes_dist_adults$th),
                        chosen_n.iter = 1000,
                        chosen_rot = "oblimin")
colnames(efa_th_adults$loadings) <- paste0("thADULTS_", 
                                           colnames(efa_th_adults$loadings))

efa_ch_adults <- fa_fun(d_ch_adults_w, 
                        n = median(pa_outcomes_dist_adults$ch),
                        chosen_n.iter = 1000,
                        chosen_rot = "oblimin")
colnames(efa_ch_adults$loadings) <- paste0("chADULTS_", 
                                           colnames(efa_ch_adults$loadings))

efa_vt_adults <- fa_fun(d_vt_adults_w, 
                        n = median(pa_outcomes_dist_adults$vt),
                        chosen_n.iter = 1000,
                        chosen_rot = "oblimin")
colnames(efa_vt_adults$loadings) <- paste0("vtADULTS_", 
                                           colnames(efa_vt_adults$loadings))
```

```{r factor names adults}
factor_names_adults <- data.frame(factor = c(colnames(efa_us_adults$loadings),
                                             colnames(efa_gh_adults$loadings),
                                             colnames(efa_th_adults$loadings),
                                             colnames(efa_ch_adults$loadings),
                                             colnames(efa_vt_adults$loadings))) %>%
  mutate(age_group = "adults") %>%
  mutate(country = case_when(grepl("^us", factor) ~ "US",
                             grepl("^gh", factor) ~ "Ghana",
                             grepl("^th", factor) ~ "Thailand",
                             grepl("^ch", factor) ~ "China",
                             grepl("^vt", factor) ~ "Vanuatu"),
         country = factor(country, levels_country)) %>%
  mutate(factor_name = gsub("^us", "US ", factor),
         factor_name = gsub("^gh", "Gh. ", factor_name),
         factor_name = gsub("^th", "Th. ", factor_name),
         factor_name = gsub("^ch", "Ch. ", factor_name),
         factor_name = gsub("^vt", "Va. ", factor_name),
         factor_name = gsub("ADULTS", "adults", factor_name),
         factor_name = gsub("_F", " Factor ", factor_name)) %>%
  mutate(factor_descript = recode(factor,
                                  usADULTS_F1 = "Body",
                                  usADULTS_F2 = "Heart",
                                  usADULTS_F3 = "Mind",
                                  ghADULTS_F1 = "Inner sphere (mind-like)",
                                  ghADULTS_F2 = "Body-like",
                                  ghADULTS_F3 = "Interpersonal, religious",
                                  thADULTS_F1 = "Body-like",
                                  thADULTS_F2 = "Heart-like",
                                  thADULTS_F3 = "Mind-like",
                                  chADULTS_F1 = "Heart-like",
                                  chADULTS_F2 = "Body-like",
                                  chADULTS_F3 = "Mind-like",
                                  vtADULTS_F1 = "Harmony (mind-like, heart-like)",
                                  vtADULTS_F2 = "Sin (body-like)"),
         factor_labdescript = paste(gsub(".*_F", "F", factor),
                                    factor_descript, sep = ": "))
```

## Factor loadings

```{r order adults}
# order capacities: adults
order_us_adults <- fa.sort(efa_us_adults)$loadings[] %>% rownames()
order_gh_adults <- fa.sort(efa_gh_adults)$loadings[] %>% rownames()
order_th_adults <- fa.sort(efa_th_adults)$loadings[] %>% rownames()
order_ch_adults <- fa.sort(efa_ch_adults)$loadings[] %>% rownames()
order_vt_adults <- fa.sort(efa_vt_adults)$loadings[] %>% rownames()
```

```{r loadings adults}
# compile loadings: adults
loadings_adults <- bind_rows(
  loadings_fun(efa_us_adults) %>% mutate(country = "US"),
  loadings_fun(efa_gh_adults) %>% mutate(country = "Ghana"),
  loadings_fun(efa_th_adults) %>% mutate(country = "Thailand"),
  loadings_fun(efa_ch_adults) %>% mutate(country = "China"),
  loadings_fun(efa_vt_adults) %>% mutate(country = "Vanuatu")) %>%
  mutate(country = factor(country, levels = levels_country),
         capacity_ord_us = factor(capacity, levels = order_us_adults),
         capacity_ord_gh = factor(capacity, levels = order_gh_adults),
         capacity_ord_th = factor(capacity, levels = order_th_adults),
         capacity_ord_ch = factor(capacity, levels = order_ch_adults),
         capacity_ord_vt = factor(capacity, levels = order_vt_adults)) %>%
  arrange(country, factor, desc(abs(loading)), capacity) %>%
  mutate(order = 1:nrow(.)) %>%
  left_join(factor_names_adults)
```

```{r heatmap adults, fig.width = 5, fig.asp = 0.7}
# make heatmap figure: adults
loadings_adults %>%
  mutate(factor_num = as.numeric(gsub(".*F", "", factor))) %>%
  mutate(sample = paste(country, "adults", sep = "\n")) %>%
  left_join(factor_names_adults) %>%
  mutate(country = factor(country, levels = levels_country)) %>%
  ggplot(aes(x = reorder(factor_labdescript, factor_num), 
             y = reorder(capacity, desc(capacity_ord_us)),
             # y = reorder(capacity, desc(capacity_ord_ec)), 
             # y = reorder(capacity, desc(capacity_ord_gh)),
             # y = reorder(capacity, desc(capacity_ord_th)),
             # y = reorder(capacity, desc(capacity_ord_ch)),
             # y = reorder(capacity, desc(capacity_ord_vt)),
             fill = loading)) +
  facet_grid(~ reorder(sample, as.numeric(country)), scales = "free", space = "free") +
  geom_tile(color = "black", size = 0.2) +
  geom_text(aes(label = format(round(loading, 2), nsmall = 2)), size = 3) +
  scale_fill_distiller(palette = "RdYlBu", limits = c(-1, 1),
                       guide = guide_colorbar(barheight = 20, barwidth = 0.5)) +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1),
        panel.spacing.x = unit(0.8, "lines"),
        strip.text.x = element_text(size = 10, face = "bold")) +
  labs(x = NULL, y = "Capacity", fill = "Factor\nloading")
```

## Congruence

```{r congruence adults}
cong_adults <- fa.congruence(x = list(efa_us_adults$loadings,
                                      efa_gh_adults$loadings,
                                      efa_th_adults$loadings,
                                      efa_ch_adults$loadings,
                                      efa_vt_adults$loadings),
                             digits = 5) %>%
  # get_upper_tri_fun() %>%
  data.frame() %>%
  rownames_to_column("factor_A") %>%
  gather(factor_B, cong, -factor_A) %>%
  left_join(factor_names_adults %>% 
              rename_all(list(~ (paste(., "A", sep = "_"))))) %>%
  left_join(factor_names_adults %>% 
              rename_all(list(~ (paste(., "B", sep = "_")))))
```

```{r top match adults}
cong_adults_top_match_A <- top_match_fun(cong_adults, "country_A")
cong_adults_top_match_B <- top_match_fun(cong_adults, "country_B")
```

```{r cong all pairs adults, fig.width = 5, fig.asp = 0.7}
cong_adults %>%
  mutate_at(#vars(contains("labdescript")),
    vars(factor_labdescript_A),
    funs(gsub(" \\(", "\n\\(", .))) %>%
  mutate_at(#vars(contains("labdescript")),
    vars(factor_labdescript_A),
    funs(gsub("\\/", "\\/\n", .))) %>%
  # left_join(cong_adults_top_match_A %>% rename(top_match_A = top_match)) %>%
  left_join(cong_adults_top_match_B %>% rename(top_match_B = top_match)) %>%
  mutate(is_top_match = case_when(factor_A == factor_B ~ "bold.italic",
                                  # factor_A == top_match_A ~ "bold",
                                  factor_B == top_match_B ~ "bold",
                                  TRUE ~ "plain")) %>%
  # mutate(cong = ifelse(cong == 1, NA_real_, cong)) %>%
  mutate(sample_A = paste(toupper(country_A), "adults", sep = ":\n")) %>%
  mutate(sample_B = paste(toupper(country_B), "adults", sep = ":\n")) %>%
  mutate_at(vars(country_A, country_B),
            funs(factor(toupper(.), levels = toupper(levels_country)))) %>%
  ggplot(aes(x = factor_labdescript_A,
             y = reorder(factor_labdescript_B, desc(factor_labdescript_B)),
             fill = cong)) +
  facet_grid(reorder(sample_B, as.numeric(country_B)) ~ 
               reorder(sample_A, as.numeric(country_A)), 
             scales = "free", space = "free") +
  geom_tile(color = "black", size = 0.2) +
  geom_text(aes(label = case_when(is.na(cong) ~ "",
                                  TRUE ~ format(round(cong, 2), nsmall = 2)),
                fontface = is_top_match,
                color = is_top_match),
            size = 3, show.legend = F) +
  scale_color_manual(values = c("darkred", "darkblue", "black")) +
  scale_fill_viridis_c(option = "viridis", 
                       guide = guide_colorbar(barwidth = 25, barheight = 0.5)) +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1),
        legend.position = "bottom",
        strip.text = element_text(size = 10, face = "bold")) +
  labs(x = NULL, y = NULL, fill = expression(italic(r[c])))
```

## Bootstrapped congruence

```{r bootstrap congruence adults}
if (file.exists("../results/cong_df_adults_oblique_drop.RDS")) {
  
  cong_df_adults <- readRDS("../results/cong_df_adults_oblique_drop.RDS")
  
} else {
  
  bs_adults <- loadings_adults %>%
    select(capacity, factor, loading) %>%
    spread(factor, loading) %>%
    select(-capacity) %>%
    sjstats::bootstrap(1000) 
  
  factors <- levels(factor(loadings_adults$factor))
  
  cong_df_adults <- data.frame(NULL)
  for (i in factors) {
    for (j in factors) {
      cname <- paste(i, j, sep = ".")
      temp <- bs_adults %>%
        mutate(cong = map_dbl(strap, ~lsa::cosine(as.data.frame(.x)[,i],
                                                  as.data.frame(.x)[,j])))
      cong_df_adults[1:1000, cname] <- temp$cong
    }
  }
  
  cong_df_adults <- cong_df_adults %>%
    gather(factor_pair, cong) %>%
    separate(factor_pair, into = c("factor_A", "factor_B"), sep = "\\.") %>%
    group_by(factor_A, factor_B) %>%
    summarise(mean = mean(cong),
              ci_lower = ci_lower(cong),
              ci_upper = ci_upper(cong)) %>%
    ungroup() %>%
    left_join(factor_names_adults %>%
                rename_all(funs(paste(., "A", sep = "_")))) %>%
    left_join(factor_names_adults %>%
                rename_all(funs(paste(., "B", sep = "_"))))
  
  rm(i, j, cname, temp, factors)
  
  saveRDS(cong_df_adults, file = "../results/cong_df_adults_oblique_drop.RDS")
}
```

```{r cong min adults}
# find minimum value to set constant lower bound of plots
min_cong_adults <- cong_df_adults %>%
  summarise(min_cong = min(ci_lower, na.rm = T))
```

```{r cong cis us base adults, fig.width = 4, fig.asp = 0.9}
# FIGURE 3
cong_plot_fun(cong_df = cong_df_adults, which_country = "US") +
  ylim(min_cong_adults$min_cong, 1) +
  # ylim(NA, 1) +
  labs(x = NULL)
ggsave("../figures/fig03_oblique.png")
```

```{r cong cis gh base adults, fig.width = 4, fig.asp = 0.9}
# FIGURE S1
cong_plot_fun(cong_df = cong_df_adults %>%
                mutate_at(#vars(contains("labdescript")),
                  vars(factor_labdescript_A),
                  funs(gsub(" \\(", "\n\\(", .))) %>%
                mutate_at(#vars(contains("labdescript")),
                  vars(factor_labdescript_A),
                  funs(gsub("\\/", "\\/\n", .))), 
              which_country = "Ghana") +
  ylim(min_cong_adults$min_cong, 1)
ggsave("../figures/figS01_oblique.png")
```

```{r cong cis th base adults, fig.width = 4, fig.asp = 0.9}
# FIGURE S2
cong_plot_fun(cong_df = cong_df_adults, 
              which_country = "Thailand") +
  ylim(min_cong_adults$min_cong, 1)
ggsave("../figures/figS02_oblique.png")
```

```{r cong cis ch base adults, fig.width = 4, fig.asp = 0.9}
# FIGURE S3
cong_plot_fun(cong_df = cong_df_adults, 
              which_country = "China") +
  ylim(min_cong_adults$min_cong, 1)
ggsave("../figures/figS03_oblique.png")
```

```{r cong cis vt base adults, fig.width = 4, fig.asp = 0.9}
# FIGURE S4
cong_plot_fun(cong_df = cong_df_adults %>%
                mutate_at(#vars(contains("labdescript")),
                  vars(factor_labdescript_A),
                  funs(gsub(" \\(", "\n\\(", .))) %>%
                mutate_at(#vars(contains("labdescript")),
                  vars(factor_labdescript_A),
                  funs(gsub("\\/", "\\/\n", .))), 
              which_country = "Vanuatu") +
  ylim(min_cong_adults$min_cong, 1)
ggsave("../figures/figS04_oblique.png")
```

```{r body mind cong adults}
# "In each sample, there was a factor that was similar to US adults’ “body” factor...
cong_df_adults %>% 
  filter(grepl("body", tolower(factor_descript_A)), 
         grepl("body", tolower(factor_descript_B)),
         country_A != "US", country_B == "US")

# "...and not similar to the US adult “mind” factor, ...
cong_df_adults %>% 
  filter(grepl("body", tolower(factor_descript_A)), 
         grepl("mind", tolower(factor_descript_B)),
         country_A != "US", country_B == "US")

# "... and a factor that was much more similar to US adults’ “mind” factor...
cong_df_adults %>% 
  filter(grepl("mind", tolower(factor_descript_A)), 
         grepl("mind", tolower(factor_descript_B)),
         country_A != "US", country_B == "US")

# "...than the US adult “body” factor."
cong_df_adults %>% 
  filter(grepl("mind", tolower(factor_descript_A)), 
         grepl("body", tolower(factor_descript_B)),
         country_A != "US", country_B == "US")
```
```{r heart cong adults}
cong_df_adults %>% 
  filter(grepl("heart", tolower(factor_descript_A)), 
         grepl("heart", tolower(factor_descript_B)),
         country_A %in% c("Thailand", "China"), country_B == "US")

cong_df_adults %>% 
  filter(grepl("body", tolower(factor_descript_A)) | 
           grepl("mind", tolower(factor_descript_A)),
         grepl("heart", tolower(factor_descript_B)),
         country_A %in% c("Thailand", "China"), country_B == "US")
```


# Children

## Samples

```{r samples children}
bind_rows(d_us_children, d_gh_children, d_th_children, d_ch_children, d_vt_children) %>%
  mutate(country = factor(country, levels = levels_country)) %>%
  distinct(country, subj_id) %>%
  count(country) %>% 
  janitor::adorn_totals()
```

## Scale use

```{r scale use mean overall children}
bind_rows(d_us_children, d_gh_children, d_th_children, d_ch_children, d_vt_children) %>%
  mutate(country = factor(country, levels = levels_country),
         response_cat = recode_factor(response_cat,
                                      "no" = "no",
                                      "kind of" = "kind of",
                                      "yes" = "yes", 
                                      .missing = "missing data")) %>%
  count(country, response_cat) %>%
  complete(response_cat, nesting(country), fill = list(n = 0)) %>%
  group_by(country) %>%
  mutate(prop = n/sum(n, na.rm = T)) %>%
  ungroup() %>%
  select(-n) %>%
  spread(response_cat, prop) %>%
  janitor::adorn_pct_formatting(digits = 2)
```

## Factor retention: parallel analysis

```{r parallel dist children, fig.width = 3, fig.asp = 0.5}
# Here's the distribution over outcomes of parallel analysis with 100 iterations. We'll choose the median number of factors.

if (file.exists("../results/pa_outcomes_dist_children_drop.RDS")) {
  
  pa_outcomes_dist_children <- readRDS("../results/pa_outcomes_dist_children_drop.RDS")
  
} else {
  
  pa_outcomes_dist_children <- data.frame(us = NULL, gh = NULL, th = NULL,
                                          ch = NULL, vt = NULL)
  
  set.seed(54321)
  n_cores <- parallel::detectCores()
  options(mc.cores = n_cores)
  
  for (i in 1:100) {
    pa_outcomes_dist_children[i, "us"] <- fa.parallel(d_us_children_w, plot = F)$nfact
    pa_outcomes_dist_children[i, "gh"] <- fa.parallel(d_gh_children_w, plot = F)$nfact     
    pa_outcomes_dist_children[i, "th"] <- fa.parallel(d_th_children_w, plot = F)$nfact
    pa_outcomes_dist_children[i, "ch"] <- fa.parallel(d_ch_children_w, plot = F)$nfact
    pa_outcomes_dist_children[i, "vt"] <- fa.parallel(d_vt_children_w, plot = F)$nfact
  }
  
  saveRDS(pa_outcomes_dist_children, file = "../results/pa_outcomes_dist_children_drop.RDS")
}

# plot
pa_outcomes_dist_children %>%
  rownames_to_column("iter") %>%
  gather(country, nfact, -iter) %>%
  mutate(country = factor(country,
                          levels = c("us", "gh", "th", "ch", "vt"),
                          labels = levels_country)) %>%
  ggplot(aes(x = nfact)) +
  facet_grid(~ country) +
  geom_bar(stat = "count") +
  scale_x_continuous(limits = c(1, max(pa_outcomes_dist_children) + 1),
                     breaks = seq(0, 100, 1)) +
  labs(x = "Number of factors suggested by fa.parallel()")
```

## Exploratory factor analysis

```{r efa children}
set.seed(54321)

# do exploratory factor analysis: children
efa_us_children <- fa_fun(d_us_children_w, 
                          n = median(pa_outcomes_dist_children$us),
                          chosen_n.iter = 1000,
                          chosen_rot = "oblimin")
colnames(efa_us_children$loadings) <- paste0("usCHILDREN_", 
                                             colnames(efa_us_children$loadings))

efa_gh_children <- fa_fun(d_gh_children_w,
                          n = median(pa_outcomes_dist_children$gh),
                          chosen_n.iter = 1000,
                          chosen_rot = "oblimin")
colnames(efa_gh_children$loadings) <- paste0("ghCHILDREN_", 
                                             colnames(efa_gh_children$loadings))

efa_th_children <- fa_fun(d_th_children_w, 
                          n = median(pa_outcomes_dist_children$th),
                          chosen_n.iter = 1000,
                          chosen_rot = "oblimin")
colnames(efa_th_children$loadings) <- paste0("thCHILDREN_", 
                                             colnames(efa_th_children$loadings))

efa_ch_children <- fa_fun(d_ch_children_w, 
                          n = median(pa_outcomes_dist_children$ch),
                          chosen_n.iter = 1000,
                          chosen_rot = "oblimin")
colnames(efa_ch_children$loadings) <- paste0("chCHILDREN_", 
                                             colnames(efa_ch_children$loadings))

efa_vt_children <- fa_fun(d_vt_children_w, 
                          n = median(pa_outcomes_dist_children$vt),
                          chosen_n.iter = 1000,
                          chosen_rot = "oblimin")
colnames(efa_vt_children$loadings) <- paste0("vtCHILDREN_", 
                                             colnames(efa_vt_children$loadings))
```

```{r factor names children}
factor_names_children <- data.frame(factor = c(colnames(efa_us_children$loadings),
                                               colnames(efa_gh_children$loadings),
                                               colnames(efa_th_children$loadings),
                                               colnames(efa_ch_children$loadings),
                                               colnames(efa_vt_children$loadings))) %>%
  mutate(age_group = "children") %>%
  mutate(country = case_when(grepl("^us", factor) ~ "US",
                             grepl("^gh", factor) ~ "Ghana",
                             grepl("^th", factor) ~ "Thailand",
                             grepl("^ch", factor) ~ "China",
                             grepl("^vt", factor) ~ "Vanuatu"),
         country = factor(country, levels_country)) %>%
  mutate(factor_name = gsub("^us", "US ", factor),
         factor_name = gsub("^gh", "Gh. ", factor_name),
         factor_name = gsub("^th", "Th. ", factor_name),
         factor_name = gsub("^ch", "Ch. ", factor_name),
         factor_name = gsub("^vt", "Va. ", factor_name),
         factor_name = gsub("CHILDREN", "children", factor_name),
         factor_name = gsub("_F", " Factor ", factor_name)) %>%
  mutate(factor_descript = recode(factor,
                                  usCHILDREN_F1 = "Body-like, negative",
                                  usCHILDREN_F3 = "Heart-like, positive",
                                  usCHILDREN_F2 = "Mind-like",
                                  ghCHILDREN_F1 = "Body-like, negative",
                                  ghCHILDREN_F2 = "Mind-like, positive",
                                  ghCHILDREN_F3 = "Pray, add, etc.",
                                  thCHILDREN_F1 = "Body-like, positive",
                                  thCHILDREN_F2 = "Heart-like, negative",
                                  thCHILDREN_F3 = "Mind-like",
                                  thCHILDREN_F4 = "Add, pray, etc.",
                                  chCHILDREN_F1 = "Heart-like",
                                  chCHILDREN_F2 = "Body-like",
                                  chCHILDREN_F3 = "Mind-like",
                                  chCHILDREN_F4 = "Pray, etc.",
                                  vtCHILDREN_F1 = "Body-like",
                                  vtCHILDREN_F2 = "Mind-like, positive",
                                  vtCHILDREN_F3 = "Heart-like, negative"),
         factor_labdescript = paste(gsub(".*_F", "F", factor),
                                    factor_descript, sep = ": "))
```

## Factor loadings

```{r order children}
# order capacities: children
order_us_children <- fa.sort(efa_us_children)$loadings[] %>% rownames()
order_gh_children <- fa.sort(efa_gh_children)$loadings[] %>% rownames()
order_th_children <- fa.sort(efa_th_children)$loadings[] %>% rownames()
order_ch_children <- fa.sort(efa_ch_children)$loadings[] %>% rownames()
order_vt_children <- fa.sort(efa_vt_children)$loadings[] %>% rownames()
```

```{r loadings children}
# compile loadings: children
loadings_children <- bind_rows(
  loadings_fun(efa_us_children) %>% mutate(country = "US"),
  loadings_fun(efa_gh_children) %>% mutate(country = "Ghana"),
  loadings_fun(efa_th_children) %>% mutate(country = "Thailand"),
  loadings_fun(efa_ch_children) %>% mutate(country = "China"),
  loadings_fun(efa_vt_children) %>% mutate(country = "Vanuatu")) %>%
  mutate(country = factor(country, levels = levels_country),
         capacity_ord_us = factor(capacity, levels = order_us_children),
         capacity_ord_gh = factor(capacity, levels = order_gh_children),
         capacity_ord_th = factor(capacity, levels = order_th_children),
         capacity_ord_ch = factor(capacity, levels = order_ch_children),
         capacity_ord_vt = factor(capacity, levels = order_vt_children)) %>%
  arrange(country, factor, desc(abs(loading)), capacity) %>%
  mutate(order = 1:nrow(.)) %>%
  left_join(factor_names_children)
```

```{r heatmap children, fig.width = 5, fig.asp = 0.7}
# make heatmap figure: children
loadings_children %>%
  mutate(factor_num = as.numeric(gsub(".*F", "", factor))) %>%
  mutate(sample = paste(country, "children", sep = "\n")) %>%
  left_join(factor_names_children) %>%
  mutate(country = factor(country, levels = levels_country)) %>%
  ggplot(aes(x = reorder(factor_labdescript, factor_num), 
             y = reorder(capacity, desc(capacity_ord_us)),
             # y = reorder(capacity, desc(capacity_ord_ec)), 
             # y = reorder(capacity, desc(capacity_ord_gh)),
             # y = reorder(capacity, desc(capacity_ord_th)),
             # y = reorder(capacity, desc(capacity_ord_ch)),
             # y = reorder(capacity, desc(capacity_ord_vt)),
             fill = loading)) +
  facet_grid(~ reorder(sample, as.numeric(country)), scales = "free", space = "free") +
  geom_tile(color = "black", size = 0.2) +
  geom_text(aes(label = format(round(loading, 2), nsmall = 2)), size = 3) +
  scale_fill_distiller(palette = "RdYlBu", limits = c(-1, 1),
                       guide = guide_colorbar(barheight = 20, barwidth = 0.5)) +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1),
        panel.spacing.x = unit(0.8, "lines"),
        strip.text.x = element_text(size = 10, face = "bold")) +
  labs(x = NULL, y = "Capacity", fill = "Factor\nloading")
```

## Congruence

See [All samples], below.

## Bootstrapped congruence

```{r bootstrap congruence children}
if (file.exists("../results/cong_df_children_oblique_drop.RDS")) {
  
  cong_df_children <- readRDS("../results/cong_df_children_oblique_drop.RDS")
  
} else {
  
  bs_children <- loadings_children %>%
    select(capacity, factor, loading) %>%
    spread(factor, loading) %>%
    full_join(loadings_adults %>%
                select(capacity, factor, loading) %>%
                spread(factor, loading)) %>%
    select(-capacity) %>%
    sjstats::bootstrap(1000) 
  
  cong_df_children <- data.frame(NULL)
  
  for (k in levels_country) {
    
    factors_children <- levels(factor(loadings_children$factor[
      loadings_children$country == k]))
    factors_adults <- levels(factor(loadings_adults$factor[
      loadings_adults$country == k]))
    
    for (i in factors_children) {
      for (j in factors_adults) {
        cname <- paste(i, j, sep = ".")
        temp <- bs_children %>%
          mutate(cong = map_dbl(strap, ~lsa::cosine(as.data.frame(.x)[,i],
                                                    as.data.frame(.x)[,j])))
        cong_df_children[1:1000, cname] <- temp$cong
      }
    }
    
    rm(i, j, cname, temp, factors_children, factors_adults)
    
  }
  
  rm(k)
  
  cong_df_children <- cong_df_children %>%
    gather(factor_pair, cong) %>%
    separate(factor_pair, into = c("factor_A", "factor_B"), sep = "\\.") %>%
    group_by(factor_A, factor_B) %>%
    summarise(mean = mean(cong),
              ci_lower = ci_lower(cong),
              ci_upper = ci_upper(cong)) %>%
    ungroup() %>%
    full_join(factor_names_children %>%
                rename_all(funs(paste(., "A", sep = "_")))) %>%
    full_join(factor_names_adults %>%
                rename_all(funs(paste(., "B", sep = "_")))) %>%
    mutate(factor_bhm_A = case_when(
      grepl("body", tolower(factor_descript_A)) ~ "Body-like\nchild factor",
      grepl("mind", tolower(factor_descript_A)) ~ "Mind-like\nchild factor",
      grepl("heart", tolower(factor_descript_A)) ~ "Heart-like\nchild factor",
      TRUE ~ "Other")) %>%
    mutate(factor_bhm_B = case_when(
      grepl("body", tolower(factor_descript_B)) ~ "Local adults:\nBody-like factor",
      grepl("mind", tolower(factor_descript_B)) ~ "Local adults:\nMind-like factor",
      grepl("heart", tolower(factor_descript_B)) ~ "Local adults:\nHeart-like factor",
      TRUE ~ "Local adults:\nOther factor"))
  
  saveRDS(cong_df_children, file = "../results/cong_df_children_oblique_drop.RDS")
}
```

```{r cong min children}
# find minimum value to set constant lower bound of plots
min_cong_children <- cong_df_children %>%
  summarise(min_cong = min(ci_lower, na.rm = T))
```

```{r cong cis children, fig.width = 4, fig.asp = 1.4}
# FIGURE 4
# fig.asp chosen to keep absolute height of y-axis relatively similar across adults and children
cong_df_children %>%
  mutate(region_A = case_when(
    country_A == "US" ~ "SF Bay Area",
    country_A == "Ghana" ~ "Cape Coast",
    country_A == "Thailand" ~ "Chiang Mai",
    country_A == "China" ~ "Shanghai",
    country_A == "Vanuatu" ~ "PV & Malekula")) %>%
  mutate(sample_A = paste(country_A, age_group_A, sep = "\n")) %>%
  mutate(lab_A = paste(paste0(region_A, ","), 
                       paste0(toupper(country_A), ":"), 
                       age_group_A, sep = "\n")) %>%
  mutate(bhm_A = case_when(
    grepl("body", tolower(factor_labdescript_A)) ~ "body",
    grepl("mind", tolower(factor_labdescript_A)) ~ "mind",
    grepl("heart", tolower(factor_labdescript_A)) ~ "heart", 
    TRUE ~ "other")) %>%
  mutate(bhm_A = factor(bhm_A, levels = c("body", "heart", "mind", "other"))) %>%
  ggplot(aes(x = reorder(factor_labdescript_A, as.numeric(bhm_A)), y = mean)) +
  facet_grid(factor_bhm_B ~ reorder(lab_A, as.numeric(country_A)), 
             scales = "free_x", space = "free_x") +
  annotate("rect", xmin = -Inf, xmax = Inf, ymin = -Inf, ymax = 0.85,
           fill = "gray20", alpha = 0.2) +
  annotate("rect", xmin = -Inf, xmax = Inf, ymin = 0.85, ymax = 0.95,
           fill = viridisLite::viridis(2, begin = 0.75/2, end = 0.75)[1], alpha = 0.2) +
  annotate("rect", xmin = -Inf, xmax = Inf, ymin = 0.95, ymax = Inf,
           fill = viridisLite::viridis(2, begin = 0.75/2, end = 0.75)[2], alpha = 0.2) +
  geom_hline(yintercept = 0.85, lty = 2, color = "gray10") +
  geom_hline(yintercept = 0.95, lty = 2, color = "gray10") +
  geom_pointrange(aes(ymin = ci_lower, ymax = ci_upper),
                  fatten = 3,
                  show.legend = F) +
  geom_text(aes(label = format(round(mean, 2), nsmall = 2),
                y = ifelse(ci_lower < 0.2, ci_upper + 0.05, ci_lower - 0.05),
                vjust = ifelse(ci_lower < 0.2, 0, 1))) +
  scale_y_continuous(breaks = seq(-1, 1, 0.2),
                     expand = expansion(add = 0.05)) +
  scale_color_brewer(palette = "Dark2", aesthetics = c("color", "fill")) +
  scale_shape_manual(values = 21:25) +
  labs(x = NULL,
       y = expression("Similarity "(italic(r[c])))) + 
  guides(color = "none", fill = "none") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1),
        legend.position = "right",
        panel.border = element_rect(fill = scales::alpha("white", 0), color = "black"),
        strip.text = element_text(size = 10, face = "bold"), 
        plot.margin = unit(c(5.5, 5.5, 5.5, 15.5), "point"))
ggsave("../figures/fig04_oblique.png")
```

```{r body mind cong children}
# "In each sample, there was a factor that was much more similar to local adults’ “body-like” factor...
cong_df_children %>% 
  filter(grepl("body", tolower(factor_bhm_A)), 
         grepl("body", tolower(factor_bhm_B)))

# "...than their “mind-like” factor, ...
cong_df_children %>% 
  filter(grepl("body", tolower(factor_bhm_A)), 
         grepl("mind", tolower(factor_bhm_B)))

# "... and a factor that was much more similar to local adults’ “mind-like” factor...
cong_df_children %>% 
  filter(grepl("mind", tolower(factor_bhm_A)), 
         grepl("mind", tolower(factor_bhm_B)))

# "...than their “body-like” factor."
cong_df_children %>% 
  filter(grepl("mind", tolower(factor_bhm_A)), 
         grepl("body", tolower(factor_bhm_B)))
```


# All samples

## Congruence

```{r congruence all samples}
cong_all <- fa.congruence(x = list(efa_us_adults$loadings,
                                   efa_gh_adults$loadings,
                                   efa_th_adults$loadings,
                                   efa_ch_adults$loadings,
                                   efa_vt_adults$loadings,
                                   efa_us_children$loadings,
                                   efa_gh_children$loadings,
                                   efa_th_children$loadings,
                                   efa_ch_children$loadings,
                                   efa_vt_children$loadings),
                          digits = 5) %>%
  # get_upper_tri_fun() %>%
  data.frame() %>%
  rownames_to_column("factor_A") %>%
  gather(factor_B, cong, -factor_A) %>%
  left_join(bind_rows(factor_names_adults %>% 
                        rename_all(funs(paste(., "A", sep = "_"))),
                      factor_names_children %>%
                        rename_all(funs(paste(., "A", sep = "_"))))) %>%
  left_join(bind_rows(factor_names_adults %>% 
                        rename_all(funs(paste(., "B", sep = "_"))),
                      factor_names_children %>%
                        rename_all(funs(paste(., "B", sep = "_")))))
```

```{r cong all pairs format}
# make wide-form version of df
cong_all_w <- cong_all %>%
  select(factor_A, factor_B, cong) %>%
  spread(factor_B, cong) %>%
  column_to_rownames("factor_A")

# treat similarity matrix as if it were the correlation matrix for hclust
row.order <- hclust(as.dist((1 - cong_all_w)/2))$order
col.order <- hclust(as.dist(t((1 - cong_all_w)/2)))$order

# re-order matrix accoring to clustering
cong_all_w <- cong_all_w[row.order, col.order] 

# for some reason reshape2::melt() works better than current tidyverse functions...
cong_all_ordered <- melt(as.matrix(cong_all_w)) %>%
  rename(factor_A_ordered = Var1, 
         factor_B_ordered = Var2,
         cong = value) %>%
  mutate(factor_A = as.character(factor_A_ordered),
         factor_B = as.character(factor_B_ordered)) %>%
  full_join(cong_all %>% select(contains("_A")) %>% distinct()) %>%
  full_join(cong_all %>% select(contains("_B")) %>% distinct()) %>%
  mutate(lab_A = paste(paste(country_A, age_group_A), factor_labdescript_A, sep = ", "),
         lab_B = paste(paste(country_B, age_group_B), factor_labdescript_B, sep = ", "))
# mutate(sample_A = paste(country_A, age_group_A, sep = ", "),
#        sample_B = paste(country_B, age_group_B, sep = ", "),
#        lab_A = paste(sample_A, factor_labdescript_A, sep = " "),
#        lab_B = paste(sample_B, factor_labdescript_B, sep = " "))
```

```{r cong all pairs plot, fig.width = 9.5, fig.asp = 0.9}
# FIGURE 2
cong_lower_lim <- ifelse(min(cong_all_ordered$cong) > -0.05, -0.05, 
                         min(cong_all_ordered$cong))
# cong_plot_colors <- c("red4", "blue4", "darkorchid4", "black")
# cong_plot_colors <- c("black", "black", "black", "black")
cong_plot_colors <- c("red4", "red4", "red4", "black")

cong_all_ordered %>%
  ggplot(aes(x = reorder(lab_A, as.numeric(factor_A_ordered)),
             y = reorder(lab_B, as.numeric(desc(factor_B_ordered))),
             fill = cong)) + 
  geom_tile(color = "black", size = 0.2) +
  geom_text(aes(label = format(round(cong, 2), nsmall = 2),
                color = case_when(cong > 0.85 ~ "a", 
                                  cong > 0.75 ~ "b",
                                  cong > 0.65 ~ "c",
                                  TRUE ~ "d")),
            show.legend = F) +
  # body-like factors
  annotate("rect", xmin = 5.5, xmax = 15.5, ymin = 16.5, ymax = 26.5,
           color = cong_plot_colors[1], size = 1.5, alpha = 0) +
  # mind-like factors
  annotate("rect", xmin = 15.5, xmax = 25.5, ymin = 6.5, ymax = 16.5,
           color = cong_plot_colors[2], size = 1.5, alpha = 0) +
  # heart-like factors
  annotate("rect", xmin = 25.5, xmax = 31.5, ymin = 0.5, ymax = 6.5,
           color = cong_plot_colors[3], size = 1.5, alpha = 0) +
  # scale_fill_viridis_c(trans = scales::exp_trans(base = exp(1)),
  #                      limits = c(cong_lower_lim, 1), 
  #                      breaks = seq(cong_lower_lim, 1, 0.05),
  #                      labels = c(format(seq(cong_lower_lim, 0.8, 0.05), nsmall = 2),
  #                                 "0.85 = moderate", "0.90", 
  #                                 "0.95 = high", "1.00"),
  #                      option = "viridis",
  #                      guide = guide_colorbar(barheight = 40)) +
  scale_fill_gradientn(#trans = scales::exp_trans(base = exp(1)),
    limits = c(cong_lower_lim, 1), 
    breaks = seq(cong_lower_lim, 1, 0.05),
    labels = c(format(seq(cong_lower_lim, 0.8, 0.05), nsmall = 2),
               "0.85 = moderate", "0.90", 
               "0.95 = high", "1.00"),
    colors = viridisLite::viridis(6),
    values = c(0, 0.65, 0.75, 0.85, 0.95, 1),
    guide = guide_colorbar(barheight = 40)) +
  scale_color_manual(values = c("black", "black", "black", "gray60")) +
  theme_minimal() +
  theme(
    axis.text.x = element_text(
      # angle = 45, hjust = 1, vjust = 1,
      angle = 90, hjust = 1, vjust = 1,
      size = size_fun(cong_all_ordered$lab_A, sizes = c(20, 14)),
      color = color_fun(cong_all_ordered$lab_A, color_list = cong_plot_colors),
      face  = face_fun(cong_all_ordered$lab_A)),
    axis.text.y = element_text(
      size = rev(size_fun(cong_all_ordered$lab_A, sizes = c(20, 14))),
      color = rev(color_fun(cong_all_ordered$lab_A, color_list = cong_plot_colors)),
      face  = rev(face_fun(cong_all_ordered$lab_A))),
    legend.title = element_text(face = "bold", size = 20),
    # axis.ticks = element_line(size = 0.5),
    axis.ticks.x = element_line(
      size = size_fun(cong_all_ordered$lab_A, sizes = c(1.5, 0.5)),
      color = color_fun(cong_all_ordered$lab_A, color_list = cong_plot_colors)),
    axis.ticks.y = element_line(
      size = rev(size_fun(cong_all_ordered$lab_A, sizes = c(1.5, 0.5))),
      color = rev(color_fun(cong_all_ordered$lab_A, color_list = cong_plot_colors))),
    axis.ticks.length = unit(0.25, "cm")) +
  labs(x = NULL, y = NULL, fill = expression(italic(r[c])))
ggsave("../figures/fig02_oblique.png")
```

## Jaccard Similarity

```{r jaccard all samples}
strong_load_all <- loadings_adults %>%
  bind_rows(loadings_children) %>%
  select(country, age_group, factor, capacity, loading) %>%
  mutate(strong_load = ifelse(loading >= 0.5, 1, 0)) %>%
  select(-loading)

cross_load_all <- strong_load_all %>%
  filter(strong_load == 1) %>%
  count(country, age_group, capacity, strong_load) %>%
  filter(n > 1) %>%
  mutate(cross_load = T) %>%
  select(country, age_group, capacity, cross_load)

strong_noncross_load_all <- strong_load_all %>%
  left_join(cross_load_all) %>%
  filter(is.na(cross_load))

jaccard_all <- strong_noncross_load_all %>%
  select(factor, capacity, strong_load) %>%
  spread(factor, strong_load) %>%
  column_to_rownames("capacity") %>%
  t() %>%
  dist(method = "binary", diag = T, upper = T) %>%
  as.matrix() %>%
  data.frame() %>%
  rownames_to_column("factor_A") %>%
  gather(factor_B, jaccard, -factor_A) %>%
  # compute similarity index instead of distance
  mutate(jaccard = 1 - jaccard) %>%
  left_join(bind_rows(factor_names_adults %>% 
                        rename_all(funs(paste(., "A", sep = "_"))),
                      factor_names_children %>%
                        rename_all(funs(paste(., "A", sep = "_"))))) %>%
  left_join(bind_rows(factor_names_adults %>% 
                        rename_all(funs(paste(., "B", sep = "_"))),
                      factor_names_children %>%
                        rename_all(funs(paste(., "B", sep = "_")))))
```

```{r jaccard all pairs format}
# make wide-form version of df
jaccard_all_w <- jaccard_all %>%
  select(factor_A, factor_B, jaccard) %>%
  spread(factor_B, jaccard) %>%
  column_to_rownames("factor_A")

# treat distance matrix as if it were the correlation matrix for hclust
row.order <- hclust(as.dist((1 - jaccard_all_w)/2))$order
col.order <- hclust(as.dist(t((1 - jaccard_all_w)/2)))$order

# re-order matrix accoring to clustering
jaccard_all_w <- jaccard_all_w[row.order, col.order] 

# for some reason reshape2::melt() works better than current tidyverse functions...
jaccard_all_ordered <- melt(as.matrix(jaccard_all_w)) %>%
  rename(factor_A_ordered = Var1, 
         factor_B_ordered = Var2,
         jaccard = value) %>%
  mutate(factor_A = as.character(factor_A_ordered),
         factor_B = as.character(factor_B_ordered)) %>%
  full_join(jaccard_all %>% select(contains("_A")) %>% distinct()) %>%
  full_join(jaccard_all %>% select(contains("_B")) %>% distinct()) %>%
  mutate(lab_A = paste(paste(country_A, age_group_A), factor_labdescript_A, sep = ", "),
         lab_B = paste(paste(country_B, age_group_B), factor_labdescript_B, sep = ", "))
# mutate(sample_A = paste(country_A, age_group_A, sep = ", "),
#        sample_B = paste(country_B, age_group_B, sep = ", "),
#        lab_A = paste(sample_A, factor_labdescript_A, sep = " "),
#        lab_B = paste(sample_B, factor_labdescript_B, sep = " "))
```

```{r jaccard all pairs plot, fig.width = 9.5, fig.asp = 0.9}
# FIGURE 2 equivalent
jaccard_lower_lim <- ifelse(min(jaccard_all_ordered$jaccard) > 0, 0, 
                         min(jaccard_all_ordered$jaccard))
# jaccard_plot_colors <- c("red4", "blue4", "darkorchid4", "black")
# jaccard_plot_colors <- c("black", "black", "black", "black")
jaccard_plot_colors <- c("red4", "red4", "red4", "black")

jaccard_all_ordered %>%
  ggplot(aes(x = reorder(lab_A, as.numeric(factor_A_ordered)),
             y = reorder(lab_B, as.numeric(desc(factor_B_ordered))),
             fill = jaccard)) + 
  geom_tile(color = "black", size = 0.2) +
  geom_text(aes(label = case_when(
    # jaccard %in% c(0, 1) ~ format(round(jaccard, 0), nsmall = 0),
    TRUE ~ format(round(jaccard, 2), nsmall = 2)),
    color = case_when(jaccard >= 0.75 ~ "a", 
                      jaccard >= 0.5 ~ "b",
                      jaccard >= 0.25 ~ "c",
                      TRUE ~ "d")),
    show.legend = F) +
  # mind-like and other factors
  annotate("rect", xmin = 0.5, xmax = 14.5, ymin = 17.5, ymax = 31.5,
           color = jaccard_plot_colors[2], size = 1.5, alpha = 0) +
  # body-like factors
  annotate("rect", xmin = 14.5, xmax = 24.5, ymin = 7.5, ymax = 17.5,
           color = jaccard_plot_colors[1], size = 1.5, alpha = 0) +
  # heart-like factors
  annotate("rect", xmin = 24.5, xmax = 31.5, ymin = 0.5, ymax = 7.5,
           color = jaccard_plot_colors[3], size = 1.5, alpha = 0) +
  scale_fill_viridis_c(#trans = scales::exp_trans(base = exp(1)),
                       limits = c(jaccard_lower_lim, 1),
                       breaks = seq(jaccard_lower_lim, 1, 0.05),
                       # labels = c(format(seq(jaccard_lower_lim, 0.8, 0.05), 
                       #                   nsmall = 2),
                       #            "0.85 = moderate", "0.90",
                       #            "0.95 = high", "1.00"),
                       option = "viridis", 
                       # direction = -1,
                       guide = guide_colorbar(barheight = 40)) +
  # scale_fill_gradientn(#trans = scales::exp_trans(base = exp(1)),
  #   limits = c(jaccard_lower_lim, 1), 
  #   breaks = seq(jaccard_lower_lim, 1, 0.05),
  #   labels = c(format(seq(jaccard_lower_lim, 0.8, 0.05), nsmall = 2),
  #              "0.85 = moderate", "0.90", 
  #              "0.95 = high", "1.00"),
  #   colors = viridisLite::viridis(6),
  #   values = c(0, 0.65, 0.75, 0.85, 0.95, 1),
  #   guide = guide_colorbar(barheight = 40)) +
  scale_color_manual(values = c("black", "black", "black", "gray60")) +
  theme_minimal() +
  theme(
    axis.text.x = element_text(
      # angle = 45, hjust = 1, vjust = 1,
      angle = 90, hjust = 1, vjust = 1,
      size = size_fun(jaccard_all_ordered$lab_A, sizes = c(20, 14)),
      color = color_fun(jaccard_all_ordered$lab_A, color_list = jaccard_plot_colors),
      face  = face_fun(jaccard_all_ordered$lab_A)),
    axis.text.y = element_text(
      size = rev(size_fun(jaccard_all_ordered$lab_A, sizes = c(20, 14))),
      color = rev(color_fun(jaccard_all_ordered$lab_A, color_list = jaccard_plot_colors)),
      face  = rev(face_fun(jaccard_all_ordered$lab_A))),
    legend.title = element_text(face = "bold", size = 20),
    # axis.ticks = element_line(size = 0.5),
    axis.ticks.x = element_line(
      size = size_fun(jaccard_all_ordered$lab_A, sizes = c(1.5, 0.5)),
      color = color_fun(jaccard_all_ordered$lab_A, color_list = jaccard_plot_colors)),
    axis.ticks.y = element_line(
      size = rev(size_fun(jaccard_all_ordered$lab_A, sizes = c(1.5, 0.5))),
      color = rev(color_fun(jaccard_all_ordered$lab_A, color_list = jaccard_plot_colors))),
    axis.ticks.length = unit(0.25, "cm")) +
  labs(x = NULL, y = NULL, fill = "Jaccard\nsimilarity")
ggsave("../figures/fig02_oblique_jaccard.png")
```

## Developmental comparisons

```{r dev comp all sites, fig.width = 4, fig.asp = 1.2}
# FIGURE S5, FIGURE S6, FIGURE S7, FIGURE S8, FIGURE S9
plot_grid(heatmap_comp_fun(
  efa_list = list(efa_us_adults, efa_us_children), padding = F),
  dev_cong_plot_fun(cong_df_children, which_country = "US", padding = T),
  ncol = 1, rel_heights = c(2, 1.5), labels = "AUTO")
ggsave("../figures/figS05_oblique.png")

plot_grid(heatmap_comp_fun(
  efa_list = list(efa_gh_adults, efa_gh_children), padding = F),
  dev_cong_plot_fun(cong_df_children, which_country = "Ghana", padding = T),
  ncol = 1, rel_heights = c(2, 1.5), labels = "AUTO")
ggsave("../figures/figS06_oblique.png")

plot_grid(heatmap_comp_fun(
  efa_list = list(efa_th_adults, efa_th_children), padding = F),
  dev_cong_plot_fun(cong_df_children, which_country = "Thailand", padding = T),
  ncol = 1, rel_heights = c(2, 1.5), labels = "AUTO")
ggsave("../figures/figS07_oblique.png")

plot_grid(heatmap_comp_fun(
  efa_list = list(efa_ch_adults, efa_ch_children), padding = F),
  dev_cong_plot_fun(cong_df_children, which_country = "China", padding = T),
  ncol = 1, rel_heights = c(2, 1.5), labels = "AUTO")
ggsave("../figures/figS08_oblique.png")

plot_grid(heatmap_comp_fun(
  efa_list = list(efa_vt_adults, efa_vt_children), padding = F),
  dev_cong_plot_fun(cong_df_children, which_country = "Vanuatu", padding = T),
  ncol = 1, rel_heights = c(2, 1.5), labels = "AUTO")
ggsave("../figures/figS09_oblique.png")
```

```{r loadings all samples, fig.width = 6.5, fig.asp = 0.6}
# FIGURE 1, version 1
heatmap_comp_fun(list(efa_us_adults, efa_gh_adults, efa_th_adults, 
                      efa_ch_adults, efa_vt_adults, 
                      efa_us_children, efa_gh_children, efa_th_children, 
                      efa_ch_children, efa_vt_children), 
                 facet_order_vars = c("age_group", "country", "fnum"),
                 facet_lab_split = T) +
  theme(panel.spacing.x = unit(c(rep(0.2, 4), 1, rep(0.2, 4)), "line"),
        legend.position = "bottom") +
  guides(fill = guide_colorbar(barwidth = 30, barheight = 0.5, 
                               title = "Factor loading", title.vjust = 1))
ggsave("../figures/fig01v1_oblique.png")
```

```{r dominant factor, fig.width = 6.5, fig.asp = 0.6, include = F}
# highlighting dominant factor (ignoring cross-loadings > 0.05)
loadings_all <- loadings_adults %>%
  select(-contains("ord")) %>%
  full_join(loadings_children %>%
              select(-contains("ord")))

dom_factors_all <- loadings_all %>%
  group_by(country, age_group, capacity) %>% 
  top_n(1, abs(loading)) %>%
  ungroup() %>%
  select(country, age_group, capacity, factor, loading) %>%
  rename(dom_factor = factor,
         dom_loading = loading)

rect_df <- loadings_all %>%
  full_join(dom_factors_all) %>%
  mutate(fnum = gsub(".*_F", "F", factor)) %>%
  select(-starts_with("factor")) %>%
  spread(fnum, loading) %>%
  mutate(diff1 = abs(dom_loading) - abs(F1),
         diff2 = abs(dom_loading) - abs(F2),
         diff3 = abs(dom_loading) - abs(F3),
         diff4 = abs(dom_loading) - abs(F4)) %>%
  select(-c(dom_loading, starts_with("F"))) %>%
  gather(which_diff, diff, starts_with("diff")) %>%
  filter(diff != 0, !is.na(diff)) %>%
  group_by(country, age_group, capacity) %>%
  top_n(-1, diff) %>%
  ungroup() %>%
  mutate(any_small = diff < 0.05) %>%
  rename(factor = dom_factor) %>%
  left_join(full_join(factor_names_adults, factor_names_children))

# analog to FIGURE 1
temp_cap_order <- fa.sort(efa_us_adults)$loadings[] %>% rownames() %>% rev()

ggplot(rect_df %>%
         filter(!is.na(any_small)) %>%
         mutate(capacity = factor(capacity, levels = temp_cap_order)),
       aes(x = factor_labdescript, 
           y = capacity, 
           fill = any_small)) +
  facet_grid(~ interaction(country, age_group), space = "free", scales = "free") +
  geom_tile() +
  theme(panel.spacing.x = unit(c(rep(0.2, 4), 1, rep(0.2, 4)), "line"),
        axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1),
        legend.position = "bottom")
# ggsave("../figures/fig01v2_oblique.png")
```

```{r loadings all samples v2, fig.width = 6.5, fig.asp = 0.6}
# FIGURE 1, version 2 (included in main text)
loadings_adults %>%
  bind_rows(loadings_children) %>%
  # select(-contains("_ord")) %>%
  mutate(factor_bhm = case_when(
    grepl("body", tolower(factor_descript)) ~ "BODY-like factors",
    grepl("mind", tolower(factor_descript)) ~ "MIND-like factors",
    grepl("heart", tolower(factor_descript)) ~ "HEART-like factors",
    TRUE ~ "Other")) %>%
  left_join(strong_noncross_load_all %>% 
              select(factor, capacity, strong_load, cross_load)) %>%
  mutate(font_face = case_when(
    strong_load == 1 & is.na(cross_load) ~ "bold",
    TRUE ~ "plain")) %>%
  ggplot(aes(x = reorder(paste(gsub("Factor ", "F", factor_name), 
                               factor_descript, sep = ": "), 
                         as.numeric(country)), 
             y = reorder(capacity_ord_us, desc(capacity_ord_us)),
             fill = loading)) +
  facet_grid(cols = vars(factor_bhm, age_group), 
             scales = "free", space = "free") +
  geom_tile(color = "black", size = 0.2) +
  geom_text(aes(label = format(round(loading, 2), nsmall = 2), 
                fontface = font_face), size = 3) +
  scale_fill_distiller(palette = "RdYlBu", limits = c(-1, 1)) +
  theme_minimal() +
  labs(x = NULL, y = NULL) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1),
        panel.spacing.x = unit(c(0.2, 1, 0.2, 1, 0.2, 1, 0.2), "line"),
        legend.position = "bottom") +
  guides(fill = guide_colorbar(barwidth = 30, barheight = 0.5, 
                               title = "Factor loading", title.vjust = 1))
  # select(country, capacity, loading) %>%
  # mutate(loading = round(loading, 2)) %>%
  # spread(country, loading)
ggsave("../figures/fig01v2_oblique.png")
```

## Variance accounted for

```{r}
Vaccounted_fun <- function(efa_name) {
  country <- gsub("efa_", "", efa_name)
  country <- gsub("_.*$", "", country)
  age_group <- case_when(grepl("adult", efa_name) ~ "adults",
                         grepl("child", efa_name) ~ "children",
                         TRUE ~ NA_character_)
  
  efa <- get(efa_name)
  res <- efa$Vaccounted %>%
    data.frame() %>%
    rownames_to_column("metric") %>%
    mutate(country = factor(country, 
                            levels = c("us", "gh", "th", "ch", "vt"),
                            labels = levels_country),
           age_group = factor(age_group, levels = c("adults", "children")))
  
  return(res)
}
```

```{r}
Vaccounted_all <- Vaccounted_fun("efa_us_adults") %>%
  full_join(Vaccounted_fun("efa_gh_adults")) %>%
  full_join(Vaccounted_fun("efa_th_adults")) %>%
  full_join(Vaccounted_fun("efa_ch_adults")) %>%
  full_join(Vaccounted_fun("efa_vt_adults")) %>%
  full_join(Vaccounted_fun("efa_us_children")) %>%
  full_join(Vaccounted_fun("efa_gh_children")) %>%
  full_join(Vaccounted_fun("efa_th_children")) %>%
  full_join(Vaccounted_fun("efa_ch_children")) %>%
  full_join(Vaccounted_fun("efa_vt_children"))
```

```{r}
Vaccounted_all %>%
  filter(metric %in% c("Proportion Var", "Proportion Explained")) %>%
  gather(factor, value, starts_with("F")) %>%
  mutate(value = round(value, 2)) %>%
  spread(country, value) %>%
  arrange(age_group, factor, metric)
```
```{r}
Vaccounted_all %>%
  filter(metric == "Cumulative Var") %>%
  gather(factor, value, starts_with("F")) %>%
  group_by(country, age_group) %>%
  top_n(1, value) %>%
  ungroup() %>%
  mutate(value = round(value, 2)) %>%
  select(metric, country, age_group, value) %>%
  spread(country, value) %>%
  arrange(age_group, metric)
```

## Interfactor correlations

```{r, include = F}
interfactor_cor_fun <- function(efa_name) {
  sample = gsub("efa_", "", efa_name)
  country = gsub("_.*$", "", sample)
  age_group = gsub("^.*_", "", sample)
  
  efa <- get(efa_name)
  
  # hacky, not sure why this works, but it's the only way i could get CIs
  df <- print(efa) %>%
    data.frame() %>%
    rownames_to_column("factor_pair") %>%
    separate(factor_pair, c("factor_A" ,"factor_B"), sep = "-") %>%
    mutate_at(vars(starts_with("factor")), ~ gsub("^.*_", "", .))
  
  # df <- efa$Phi %>%
  #   data.frame() %>%
  #   rownames_to_column("factor_A")
  # gather(factor_B, phi, -factor_A)
  
  df <- df %>%
  mutate_at(vars(starts_with("factor")),
            ~ paste0(country, toupper(age_group), "_", .)) %>%
    mutate(country = factor(country,
                            levels = c("us", "gh", "th", "ch", "vt"),
                            labels = levels_country),
           age_group = factor(age_group, levels = c("adults", "children")))
  
  return(df)
}
```

```{r, results = "hide", , include = F}
d_phi <- bind_rows(interfactor_cor_fun("efa_us_adults"),
                   interfactor_cor_fun("efa_gh_adults"),
                   interfactor_cor_fun("efa_th_adults"),
                   interfactor_cor_fun("efa_ch_adults"),
                   interfactor_cor_fun("efa_vt_adults"),
                   interfactor_cor_fun("efa_us_children"),
                   interfactor_cor_fun("efa_gh_children"),
                   interfactor_cor_fun("efa_th_children"),
                   interfactor_cor_fun("efa_ch_children"),
                   interfactor_cor_fun("efa_vt_children"))
```

```{r, include = F}
d_phi <- d_phi %>%
  full_join(d_phi %>%
              rename_all(~ gsub("factor_A", "factor_C", .)) %>%
              rename_all(~ gsub("factor_B", "factor_D", .)) %>%
              rename_all(~ gsub("factor_D", "factor_A", .)) %>%
              rename_all(~ gsub("factor_C", "factor_B", .))) %>% distinct()
```

```{r, include = F}
d_phi <- d_phi %>%
  select(-country, -age_group) %>%
  left_join(factor_names_adults %>%
              full_join(factor_names_children) %>%
              rename_all(~paste0(., "_A"))) %>%
  mutate(factor_bhm_A = case_when(
    grepl("body", tolower(factor_descript_A)) ~ "Body-like factor",
    grepl("mind", tolower(factor_descript_A)) ~ "Mind-like factor",
    grepl("heart", tolower(factor_descript_A)) ~ "Heart-like factor",
    TRUE ~ "Other")) %>%
  left_join(factor_names_adults %>%
              full_join(factor_names_children) %>%
              rename_all(~paste0(., "_B"))) %>%
  mutate(factor_bhm_B = case_when(
    grepl("body", tolower(factor_descript_B)) ~ "Body-like factor",
    grepl("mind", tolower(factor_descript_B)) ~ "Mind-like factor",
    grepl("heart", tolower(factor_descript_B)) ~ "Heart-like factor",
    TRUE ~ "Other")) %>%
  mutate_at(vars(factor_bhm_A, factor_bhm_B),
            ~ factor(., levels = c("Body-like factor",
                                   "Heart-like factor",
                                   "Mind-like factor",
                                   "Other"))) %>%
  select(-country_B, -age_group_B) %>%
  rename(country = country_A, age_group = age_group_A)
```

```{r, fig.width = 4, fig.asp = 1, , include = F}
d_phi %>%
  ggplot(aes(x = factor_bhm_A, 
             y = reorder(factor_bhm_B, desc(factor_bhm_B)), 
             fill = estimate)) +
  facet_grid(country ~ age_group, scales = "free", space = "free") +
  geom_tile(color = "black") +
  geom_text(aes(label = format(round(estimate, 2), nsmall = 2))) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1)) +
  labs(x = NULL, y = NULL, fill = quote(phi))
```

```{r, fig.width = 4, fig.asp = 0.6, include = F}
d_phi %>%
  ggplot(aes(x = factor_bhm_A, 
             color = country,
             # shape = age_group,
             y = estimate)) +
  geom_hline(yintercept = 0, lty = 5, color = "gray50") +
  facet_grid(age_group ~ factor_bhm_B, scales = "free_x", space = "free") +
  geom_pointrange(aes(ymin = lower, ymax = upper),
                  position = position_dodge(width = 0.5),
                  fatten = 2) +
  # geom_text(aes(label = format(round(estimate, 2), nsmall = 2))) +
  scale_color_brewer(palette = "Dark2") +
  scale_y_continuous(limits = c(NA, 1), 
                     breaks = seq(0, 1, 0.5)) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1)) +
  labs(x = NULL, y = quote(phi))
```

```{r, fig.width = 5, fig.asp = 0.7, include = F}
d_phi %>%
  ggplot(aes(x = country, 
             color = country,
             group = age_group, shape = age_group,
             y = estimate)) +
  facet_grid(factor_bhm_A ~ factor_bhm_B, scales = "free_x", space = "free") +
  geom_hline(yintercept = 0, lty = 2, color = "grey50") +
  geom_pointrange(aes(ymin = lower, ymax = upper),
                  position = position_dodge(width = 0.5)) +
  scale_color_brewer(palette = "Dark2") +
  # geom_text(aes(y = ifelse(age_group == "adults", 
  #                          estimate + 0.1,
  #                          estimate - 0.05),
  #               label = format(round(estimate, 2), nsmall = 2)),
  #           position = position_dodge(width = 0.5)) +
  scale_y_continuous(limits = c(NA, 1), 
                     breaks = seq(0, 1, 0.5)) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1)) +
  labs(x = NULL, y = quote(phi), 
       shape = "Age group", size = "Age group", 
       color = "Site")
```

```{r}
cat("US ADULTS\n")
efa_us_adults$Phi
# (efa_us_adults$Phi)^2

cat("\nUS CHILDREN\n")
efa_us_children$Phi
# (efa_us_children$Phi)^2
```

```{r}
cat("GHANA ADULTS\n")
efa_gh_adults$Phi
# (efa_gh_adults$Phi)^2

cat("\nGHANA CHILDREN\n")
efa_gh_children$Phi
# (efa_gh_children$Phi)^2
```

```{r}
cat("THAILAND ADULTS\n")
efa_th_adults$Phi
# (efa_th_adults$Phi)^2

cat("\nTHAILAND CHILDREN\n")
efa_th_children$Phi
# (efa_th_children$Phi)^2
```

```{r}
cat("CHINA ADULTS\n")
efa_ch_adults$Phi
# (efa_ch_adults$Phi)^2

cat("\nCHINA CHILDREN\n")
efa_ch_children$Phi
# (efa_ch_children$Phi)^2
```

```{r}
cat("VANUATU ADULTS\n")
efa_vt_adults$Phi
# (efa_vt_adults$Phi)^2

cat("\nVANUATU CHILDREN\n")
efa_vt_children$Phi
# (efa_vt_children$Phi)^2
```

